Search Results (936)

Background/Objectives: Multiple sclerosis (MS) often leads to gait impairments, even in early stages, and can affect autonomy and quality of life. Traditional assessment methods, while widely used, have been criticized because they lack sensitivity to subtle gait changes. This scoping review aims to map the landscape of advanced gait analysis technologies—both wearable and non-wearable—and evaluate their application in detecting, characterizing, and monitoring possible gait dysfunction in individuals with MS. Methods: A systematic search was conducted across PubMed and Scopus databases for peer-reviewed studies published in the last decade. Inclusion criteria focused on original human research using technological tools for gait assessment in individuals with MS. Data from 113 eligible studies were extracted and categorized based on gait parameters, technologies used, study design, and clinical relevance. Results: Findings highlight a growing integration of advanced technologies such as inertial measurement units, 3D motion capture, pressure insoles, and smartphone-based tools. Studies primarily focused on spatiotemporal parameters, joint kinematics, gait variability, and coordination, with many reporting strong correlations to MS subtype, disability level, fatigue, fall risk, and cognitive load. Real-world and dual-task assessments emerged as key methodologies for detecting subtle motor and cognitive-motor impairments. Digital gait biomarkers, such as stride regularity, asymmetry, and dynamic stability demonstrated high potential for early detection and monitoring. Conclusions: Advanced gait analysis technologies can provide a multidimensional, sensitive, and ecologically valid approach to evaluating and detecting motor function in MS. Their clinical integration supports personalized rehabilitation, early diagnosis, and long-term disease monitoring. Future research should focus on standardizing metrics, validating digital biomarkers, and leveraging AI-driven analytics for real-time, patient-centered care. Full article

In recent years, there have been significant advances in the application of technology in professional kitchens. This evolution of “smart kitchens” has transformed the food processing sector, ensuring higher standards of food safety through continual microbial monitoring, quality control, and hygiene improvements. This review critically discusses the recent developments in technology in commercial kitchens, focusing on their impact on microbial safety, operational efficiency, and sustainability. The literature was sourced from peer-reviewed journals, industry publications, and regulatory documents published between 2000 and 2025, selected for their relevance to the assurance of food safety using emerging technologies especially for use in commercial kitchens. Some of the most significant of these technologies currently being employed in smart kitchens include the following: smart sensors and IoT devices, artificial intelligence and machine learning systems, blockchain-based traceability technology, robotics and automation, and wearable monitoring devices. The review evaluated these technologies against criteria such as adherence to existing food safety regulations, ease of integration, cost factors, staff training requirements, and consumer perception. It is shown that these innovations will significantly enhance hygiene control, reduce the levels of waste, and increase business revenue. However, they are constrained by high installation costs, integration complexity, lack of standardized assessment measures, and the need for harmonizing automation with human oversight. Thus, for the widespread and effective uptake of these technologies, there is a need for better collaboration between policymakers, food experts, and technology innovators in creating scalable, affordable, and regulation-compliant solutions. Overall, this review provides a consolidated evidence base and practical insights for stakeholders seeking to implement advanced microbial safety technologies in professional kitchens, highlighting both current capabilities and future research opportunities. Full article

The health risks of PM_2.5-bound metals highlight the need for burden assessment, metal prioritization, and key factor analysis to support effective air quality management, yet relevant studies remain limited. Shandong Province is one of the most polluted regions in northern China, providing an ideal setting for this investigation. We monitored 17 PM_2.5-bound metals for three years across Shandong, China and performed disease burden assessment based on disability-adjusted life years (DALYs). Furthermore, key influencing factors contributing to high-hazard metals were identified through explainable machine learning. The results showed that PM_2.5-bound metal concentrations were generally higher in inland areas than in coastal regions, with Ni concentrations elevated in coastal areas. K, Ca, Zn, and Mn exhibited the highest three-year average concentrations among the metals, while Cr averaged 6.12 ng/m³, significantly exceeding the recommended annual limit of 0.025 ng/m³ set by Chinese Ambient Air Quality Standards. Jinan carried the greatest burden at 4.67 DALYs per 1000 people, followed by Zibo (3.78), Weifang (2.98), and Rizhao (2.80). CKD, interstitial pneumonia, and chronic respiratory diseases account for the highest DALYs from PM_2.5-bound metals in Shandong Province. Industrial emissions are the largest contributors to the disease burden (>34%), with Cr, Cd, and Pb as the primary contributing metals requiring priority control. Fractional vegetation cover was identified as the key factor contributing to the reduction in their concentrations. These results underscore that prioritizing the regulation of industrial combustion, particularly concerning Cr, Cd, and Pb, and enhancing fractional vegetation cover could reduce disease burden and provide public health benefits. Full article

In this multicenter, nonrandomised comparative study, we evaluated the potential effectiveness of a program to promote the safe use of dipeptidyl peptidase-4 (DPP-4) inhibitors led by community pharmacists. The program facilitated early detection of gastrointestinal adverse events (GIAEs) in patients newly prescribed DPP-4 inhibitors and facilitated timely communication with physicians. Community pharmacists reviewed patient conditions and provided relevant information to physicians as needed. GIAE monitoring based on the program was conducted in 35 patients at 10 pharmacies in Japan (intervention group) between March and August 2024. The proportion of pharmacist interventions was compared with that in 451 patients from March to August 2023, before program implementation (baseline cohort). The primary outcome, pharmacist intervention rate, was significantly higher in the intervention group (5 out of 35 patients, 14.3%) than in the baseline cohort (0 out of 451 patients, 0.0%) (p < 0.001). GIAEs were identified in 13 out of 35 patients (37.1%) in the intervention group; information for five patients (14.3%) was shared with physicians, resulting in discontinuation of the DPP-4 inhibitor in one patient and addition of supportive therapy in others. Most GIAEs occurred within the first 1–2 weeks of therapy, highlighting the need for early intervention. Thus, proactive involvement of community pharmacists may improve the care process in these cases and contribute to healthcare coordination and diabetes care quality. Full article

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. In the digital era, Internet of Things (IoT) technology has been applied to improve the effectiveness of real-time monitoring of water turbidity. This study aims to examine IoT-based water turbidity monitoring strategies and technologies using the Systematic Literature Review (SLR) method with the PRISMA protocol. In the process of searching for literature, this study identified 222 articles from the Scopus database, which, after going through the screening stage based on relevance, document type, and accessibility, resulted in seven main articles for further analysis. The results of the review show that the utilization of IoT sensors and wireless communication enables real-time monitoring of water turbidity, improves early detection of pollution, and improves effectiveness in water monitoring. However, challenges such as data security, sensor reliability, and communication network stability still need to be overcome to ensure the system works optimally. This study confirms that IoT can be a more efficient and sustainable solution in monitoring water turbidity. Full article

Background/Objective: Sacubitril/Valsartan are a combination drug approved for heart failure treatment, known to enhance natriuretic peptide activity and inhibit the renin–angiotensin–aldosterone system (RAAS). While its clinical efficacy is well-established, its broader impact on human metabolism remains insufficiently characterized. This study aimed to explore the time-resolved metabolic changes induced by Sacubitril/Valsartan in healthy individuals using an untargeted metabolomics approach. Methods: Fourteen healthy male volunteers received a single oral dose of Sacubitril/Valsartan (200 mg; 97.2 mg Sacubitril and 102.8 mg Valsartan) across two phases separated by a two-week washout period. Plasma samples were collected at eight individualized time points based on pharmacokinetic profiles. Metabolites were extracted and analyzed using high-resolution liquid chromatography–mass spectrometry (LC-QToF HRMS). Data processing included peak alignment, annotation via HMDB and METLIN, and statistical modeling through multivariate (PLS-DA, OPLS-DA) and univariate (ANOVA with FDR correction) analyses. Results: Out of 20,472 detected features, 13,840 were retained after quality filtering. A total of 315 metabolites were significantly dysregulated (FDR p < 0.05), of which 31 were confidently annotated as endogenous human metabolites. Among these, key changes were observed in the pyrimidine metabolism pathway, particularly elevated levels of uridine triphosphate (UTP) associated with cellular proliferation and metabolic remodeling. OPLS-DA models demonstrated clear separation between pre-dose and Cmax samples (R²Y = 0.993, Q² = 0.768), supporting the robustness of the time-dependent effects. Conclusions: This is the first study to characterize the dynamic metabolomic signature of Sacubitril/Valsartan in healthy humans. The findings reveal a distinctive perturbation in pyrimidine metabolism, suggesting possible links to drug mechanisms relevant to cardiac cell cycle regulation. These results underscore the utility of untargeted pharmacometabolomics in uncovering systemic drug effects and highlight potential biomarkers for monitoring therapeutic response or guiding precision treatment strategies in heart failure. Full article

The increasing adoption of industrialized offsite construction (IOC) offers substantial benefits in efficiency, quality, and sustainability, yet presents persistent challenges related to data fragmentation, real-time monitoring, and coordination. This systematic review investigates the transformative role of artificial intelligence (AI)-enhanced digital twins (DTs) in addressing these challenges within IOC. Employing a hybrid re-view methodology—combining scientometric mapping and qualitative content analysis—52 relevant studies were analyzed to identify technological trends, implementation barriers, and emerging research themes. The findings reveal that AI-driven DTs enable dynamic scheduling, predictive maintenance, real-time quality control, and sustainable lifecycle management across all IOC phases. Seven thematic application clusters are identified, including logistics optimization, safety management, and data interoperability, supported by a layered architectural framework and key enabling technologies. This study contributes to the literature by providing an early synthesis that integrates technical, organizational, and strategic dimensions of AI-driven DT implementation in IOC context. It distinguishes DT applications in IOC from those in onsite construction and expands AI’s role beyond conventional data analytics toward agentive, autonomous decision-making. The proposed future research agenda offers strategic directions such as the development of DT maturity models, lifecycle-spanning integration strategies, scalable AI agent systems, and cost-effective DT solutions for small and medium enterprises. Full article

Background: Cystic fibrosis (CF) is a multisystemic disorder caused by CFTR gene mutations, leading to impaired protein function and affecting pulmonary, gastrointestinal, hepatobiliary, skeletal, and nutritional health. The advent of CFTR modulators—especially the triple therapy elexacaftor/tezacaftor/ivacaftor (ETI)—has revolutionized clinical management, offering genotype-specific benefits beyond pulmonary outcomes. Pediatric gastroenterologists must now recognize and address emerging gastrointestinal and nutritional challenges introduced by modulator therapy. Methods: A narrative review was conducted to assess the impact of CFTR modulators on gastrointestinal function, nutritional status, bone health, and hepatobiliary involvement in pediatric patients. A structured literature search was performed using PubMed, EMBASE, and Scopus databases. Filters included articles in English or Spanish. Following full-text review based on relevance and quality, 68 articles were selected for inclusion in this review. Results: CFTR modulators have demonstrated potential improvements in gastrointestinal function, nutrient absorption, weight gain, and bone mineral density. In pediatric populations, ETI therapy has been associated with early increases in lean mass, enhanced vitamin levels, and promising trends in bone microarchitecture. However, variable outcomes regarding liver function and bone mineral density highlight the need for careful monitoring. Conclusions: While CFTR modulators present novel opportunities for systemic improvement in CF, their long-term impact on digestive and skeletal health in children remains under investigation. Pediatric gastroenterologists play a pivotal role in monitoring nutritional and hepatobiliary outcomes, optimizing treatment plans, and guiding personalized care strategies in the era of CFTR modulation. Full article

Water quality monitoring and evaluation are essential across multiple sectors, including public health, environmental protection, agriculture and livestock management, industrial processes, and broader sustainability efforts. Conventional water analysis techniques, although accurate, are often constrained by their labor-intensive nature, extended processing times, and limited applicability for in situ, real-time monitoring. In recent years, spectroscopy-based methods have gained prominence as alternatives for water quality assessment, particularly when combined with chemometric analyses and advanced technological systems. This review provides an overview of the current advancements of spectroscopy-based water monitoring, with a focus on spectroscopy techniques operating within ultraviolet–visible (UV–Vis) and infrared (IR) spectral regions, which are currently applied for the assessment of a broad range of physicochemical and biological parameters relevant to livestock water management, including chemical oxygen demand (COD), dissolved organic carbon (DOC), nitrates, microbial contamination, and heavy metal ions. The findings highlight the growing utility of spectroscopy as a reliable tool in water quality assessment (e.g., COD detection with R² = 0.86 and nitrate detection with R² = 0.95 compared to traditional methods) and underpin the need for continued research into scalable, sensor-integrated solutions tailored for use in livestock farming environments. Full article

This study investigates long-term urban air quality and population-level exposure in 33 European capital cities between 2010 and 2024. Using over 3.5 million hourly observations retrieved from official monitoring networks, city-level Air Quality Index (AQI) values were calculated and analyzed for exceedance frequency, seasonal dynamics, and spatial disparities. To account for public health relevance and sustainability implications, the analysis incorporated population-weighted exposure indicators reflecting both pollution severity and urban demographic scale. The results reveal substantial differences in air quality across the continent: cities such as Oslo and Reykjavik consistently maintain low AQI levels, while Sarajevo, Lisboa, and Madrid experience frequent exceedances. Notably, Paris shows the highest cumulative population exposure despite moderate pollution intensity, highlighting how urban density amplifies public health burdens. The use of harmonized AQI and exposure-adjusted metrics enables standardized comparisons across cities and supports sustainability-oriented urban planning. By quantifying unequal exposure burdens across Europe’s capitals, this study contributes to the evidence base for Sustainable Development Goal 11, emphasizing the need for data-informed air quality policies that address both environmental risks and population vulnerability. Full article

Wire Arc Additive Manufacturing holds promise for on-board metal part production in maritime settings, yet its implementation remains limited due to the vibrational instability inherent to shipborne environments. This study addresses this critical technological barrier by analyzing the effects of marine vibrations on process stability and proposing an integrated solution based on adaptive process control, gyrostabilized platforms, and real-time monitoring systems. The research establishes specific technical requirements for WAAM instrumentation under maritime conditions and evaluates the capabilities and limitations of existing hardware and software tools. A set of engineering recommendations is presented for improving digital modeling, thermal–mechanical monitoring, and feedback control systems. Additionally, the study highlights material-related challenges by examining the influence of alloy properties on print quality under dynamic loads. The proposed approach enhances WAAM process resilience, laying the groundwork for reliable, high-quality additive manufacturing at sea. These findings are particularly relevant to shipboard maintenance, repair, and remote fabrication tasks, marking a significant step toward the industrial adoption of WAAM in marine engineering. Full article

Effective noise elimination is essential for ensuring data reliability in high-accuracy measurement systems. However, selecting the optimal denoising strategy for diverse and non-stationary signal types remains a major challenge. This study presents a wavelet-based denoising optimization framework that systematically identifies and applies the most suitable noise reduction model for each signal segment. By evaluating multiple wavelet types and thresholding strategies, the proposed method enables adaptive and automated selection tailored to the specific characteristics of each signal. The framework was validated using synthetic, open-access, and experimentally acquired signals in both reference-based and reference-free scenarios. Extensive testing covered signals from power quality disturbance (PQD) events, electrocardiogram (ECG) data, and electroencephalogram (EEG) recordings, all of which represent critical applications where signal integrity under noise is essential. The method achieved optimal model selection in 22.15 s (across 4558 iterations) on a standard PC, with an average denoising time of 4.86 ms per signal window. These results highlight its potential for real-time and embedded applications, including smart grid monitoring systems, wearable health devices, and automated biomedical diagnostic platforms, where adaptive, fast, and reliable denoising is vital. The framework’s versatility makes it highly relevant for deployment in smart grid monitoring systems and intelligent energy infrastructures requiring robust signal conditioning. Full article

Table grapes (fresh Vitis vinifera L. fruit) rank among the top five fruit crops worldwide, yet their high perishability poses significant challenges for postharvest handling and storage. This review offers a comprehensive analysis of current and emerging preservation strategies—including chemical fumigation, irradiation, packaging technologies, controlled-atmosphere (CA) storage, biodegradable coatings, and synergistic preservation systems. Distinct from prior studies that typically emphasize specific techniques or treatment categories, this work integrates mechanistic insights with technological advancements and industrial practices across multiple preservation modalities. It further evaluates the comparative effectiveness, limitations, and practical relevance of these strategies along the supply chain. Importantly, it identifies critical research gaps—such as the lack of cultivar-specific preservation protocols, the need for low-residue and environmentally sustainable treatments, and the absence of real-time quality monitoring systems. Addressing these gaps is essential for developing next-generation solutions. Finally, this review highlights practical implications by offering a forward-looking framework to guide innovation, providing grape producers and supply chain stakeholders with strategies to minimize losses, preserve quality, and enhance market competitiveness. Full article

Fermentation plays a pivotal role in shaping the flavor and overall quality of Pu-erh tea, a microbially fermented dark tea. Here, we monitored physicochemical properties, chemical constituents, and microbial succession at 15 fermentation time points. Amplicon sequencing identified Staphylococcus, Bacillus, Kocuria, Aspergillus, Blastobotrys, Thermomyces, and Rasamsonia as dominant genera, with prokaryotic communities showing greater richness and diversity than eukaryotic ones. Beta diversity and clustering analyses revealed stable microbial structures during late fermentation stages. Non-targeted metabolomics detected 347 metabolites, including 56 significantly differential compounds enriched in caffeine metabolism and unsaturated fatty acid biosynthesis. Fermentation phases exhibited distinct metabolic patterns, with volatile aroma compounds (2-acetyl-1-pyrroline, 2,5-dimethylpyrazine) and health-beneficial fatty acids (linoleic acid, arachidonic acid) accumulating in later stages. OPLS-DA and KEGG PATHWAY analyses confirmed significant shifts in metabolite profiles relevant to flavor and biofunctionality. RDA revealed strong correlations between microbial taxa, environmental parameters, and representative metabolites. To functionally verify microbial contributions, 17 bacterial and 10 fungal strains were isolated. Six representative strains, mainly Bacillus and Aspergillus, exhibited high enzymatic activity on macromolecules, confirming their roles in polysaccharide and protein degradation. This integrative multi-omics investigation provides mechanistic insights into Pu-erh tea fermentation and offers a scientific basis for microbial community optimization in tea processing. Full article

Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts. Full article